Vision Transformers (ViT)

In this quest, we dive deep into the sourcecode of the transformers package, specifically into the file carrying Vision Transformers (ViT) model. But first, a little bit about the Vision Transformer.

Historically, Convolutional Neural Networks (CNNs) dominated the scene, renowned for their proficiency in handling image data. However, the emergence of ViT introduces a new approach, extending the transformative power of transformers, previously acclaimed in natural language processing, to the visual domain.

The introduction of ViT had broad implications for deep learning. It challenged the long-held dominance of CNNs in image-related tasks, suggesting a potential shift towards more flexible architectures like transformers. Its success also opens avenues for cross-pollination between NLP and computer vision, providing insights into how techniques developed in one domain can be adapted for another.

Transformers revolutionized natural language processing by efficiently handling sequences of data and capturing long-range dependencies.

ViT adapts this concept to images. In ViT, an image is split into a sequence of fixed-size patches, akin to words in a sentence. These patches are then linearly embedded and processed through a series of transformer blocks, each consisting of multi-head self-attention and feedforward neural networks.

ViT’s novelty lies in its departure from the inductive biases inherent in CNNs - locality and translation invariance. Instead, it relies on self-attention mechanisms to weigh the importance of different patches of an image, enabling it to dynamically focus on relevant parts of the image irrespective of their spatial location.

This ability to capture global dependencies across the image sets ViT apart and contributes to its impressive performance on various image recognition tasks.

Why Vision Transformers are Important

1. Performance: ViT has achieved state-of-the-art results on numerous benchmark datasets, outperforming traditional CNNs in many cases.
2. Scalability: ViT's architecture scales well with the increase in data and compute resources, often becoming more effective as model size grows.
3. Interpretability: The self-attention mechanism in ViT provides a level of interpretability. It allows us to visualize and understand which parts of the image the model focuses on, making it a valuable tool for tasks requiring explainability.
4. Innovation: ViT paves the way for new research directions, inspiring novel architectures and hybrid models that combine the strengths of CNNs and transformers.